library(tidyverse) # for data cleaning and plotting
library(lubridate) # for date manipulation
library(openintro) # for the abbr2state() function
library(palmerpenguins)# for Palmer penguin data
library(maps) # for map data
library(ggmap) # for mapping points on maps
library(gplots) # for col2hex() function
library(RColorBrewer) # for color palettes
library(sf) # for working with spatial data
library(leaflet) # for highly customizable mapping
library(carData) # for Minneapolis police stops data
library(ggthemes) # for more themes (including theme_map())
theme_set(theme_minimal())
# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")
starbucks_us_by_state <- Starbucks %>%
filter(Country == "US") %>%
count(`State/Province`) %>%
mutate(state_name = str_to_lower(abbr2state(`State/Province`)))
# Lisa's favorite St. Paul places - example for you to create your own data
favorite_stp_by_lisa <- tibble(
place = c("Home", "Macalester College", "Adams Spanish Immersion",
"Spirit Gymnastics", "Bama & Bapa", "Now Bikes",
"Dance Spectrum", "Pizza Luce", "Brunson's"),
long = c(-93.1405743, -93.1712321, -93.1451796,
-93.1650563, -93.1542883, -93.1696608,
-93.1393172, -93.1524256, -93.0753863),
lat = c(44.950576, 44.9378965, 44.9237914,
44.9654609, 44.9295072, 44.9436813,
44.9399922, 44.9468848, 44.9700727)
)
#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")
If you were not able to get set up on GitHub last week, go here and get set up first. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):
keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).Put your name at the top of the document.
For ALL graphs, you should include appropriate labels.
Feel free to change the default theme, which I currently have set to theme_minimal().
Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!
When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.
These exercises will reiterate what you learned in the “Mapping data with R” tutorial. If you haven’t gone through the tutorial yet, you should do that first.
ggmap)Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization?world <- get_stamenmap(
bbox = c(left = -180, bottom = -57, right = 179, top = 82.1),
maptype = "terrain",
zoom = 2)
ggmap(world) +
geom_point(data = Starbucks,
aes(x = Longitude, y = Latitude, color = `Ownership Type`),
alpha = .3,
size = .2) +
labs(title = "Starbucks Locations Worldwide by Ownership Type",
x = "Longitude",
y = "Latitude")
theme_map()
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A lot of the Starbucks locations are either Company owned or Licensed, but an interesting majority of locations are Joint Venture in Japan and Eastern China.
world <- get_stamenmap(
bbox = c(left = -93.5538, bottom = 44.8190, right = -92.8444, top = 45.1250),
maptype = "terrain",
zoom = 12)
ggmap(world) +
geom_point(data = Starbucks,
aes(x = Longitude, y = Latitude),
alpha = 1,
size = 1) +
labs(title = "Starbucks Locations in the Twin Cities Metro Area",
x = "Longitude",
y = "Latitude")
theme_map()
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world <- get_stamenmap(
bbox = c(left = -93.5538, bottom = 44.8190, right = -92.8444, top = 45.1250),
maptype = "terrain",
zoom = 9)
ggmap(world) +
geom_point(data = Starbucks,
aes(x = Longitude, y = Latitude),
alpha = 1,
size = 1) +
labs(title = "Starbucks Locations in the Twin Cities Metro Area",
x = "Longitude",
y = "Latitude")
theme_map()
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## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi TRUE
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The zoom moves further out the higher the number. I had the zoom set to 1 at first and couldn’t see anything but a few blobs of color.
get_stamenmap() in help and look at maptype). Include a map with one of the other map types.world <- get_stamenmap(
bbox = c(left = -93.5538, bottom = 44.8190, right = -92.8444, top = 45.1250),
maptype = "toner",
zoom = 12)
ggmap(world) +
geom_point(data = Starbucks,
aes(x = Longitude, y = Latitude, color = "darkgreen"),
alpha = 1,
size = 1) +
labs(title = "Starbucks Locations in the Twin Cities Metro Area",
x = "Longitude",
y = "Latitude")
theme_map()
## List of 93
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## ..- attr(*, "class")= chr [1:2] "element_line" "element"
## $ rect :List of 5
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annotate() function (see ggplot2 cheatsheet).world <- get_stamenmap(
bbox = c(left = -93.5538, bottom = 44.8190, right = -92.8444, top = 45.1250),
maptype = "terrain",
zoom = 12)
ggmap(world) +
geom_point(data = Starbucks,
aes(x = Longitude, y = Latitude, color = "darkgreen"),
alpha = 1,
size = 1) +
annotate("text", x = -93.169101, y = 44.938080, label= "Macalester College", color = "darkblue", size = 1.5)+
annotate("point", x = -93.169101, y = 44.938080, label= "Macalester College", color = "darkblue", size = 1.5) +
labs(title = "Starbucks Locations in the Twin Cities Metro Area, including Macalester College",
x = "Longitude",
y = "Latitude")
theme_map()
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geom_map())The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.
census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>%
separate(state, into = c("dot","state"), extra = "merge") %>%
select(-dot) %>%
mutate(state = str_to_lower(state))
starbucks_with_2018_pop_est <-
starbucks_us_by_state %>%
left_join(census_pop_est_2018,
by = c("state_name" = "state")) %>%
mutate(starbucks_per_10000 = (n/est_pop_2018)*10000)
dplyr review: Look through the code above and describe what each line of code does.line 183 - reads in the census population information for 2018 line 184 - separates out data into more columns, dot and state become their own columns, merge keeps it tidy when there are a lot of pieces line 185 - drops the dot colum (minus sign) line 186 - makes all of the state names lowercase lines 188-189 - start of new dataset called starbucks_with_2018_pop_est by feeding in starbucks_us_by_state information line 190 - left join is going to merge tables (in this case census_pop_est_2018) and return all values from first table and any matching values from second table line 191 - telling the join to match up along the states columns line 192 - creates new column with the number of Starbucks locations divided by population times 10,000 to find proportion of Starbucks per 10,000 people
starbucks_with_2018_pop_est<- starbucks_us_by_state %>%
left_join(census_pop_est_2018,
by = c("state_name" = "state")) %>%
mutate(starbucks_per_10000 = (n/est_pop_2018)*10000) %>%
filter(state_name != c("alaska, hawaii"))
Starbucks_US <- Starbucks %>%
filter(Country == "US",
!`State/Province` %in% c("AK", "HI"))
states_map <- map_data("state")
starbucks_with_2018_pop_est %>%
ggplot() +
geom_map(map = states_map,
aes(map_id = state_name,
fill = starbucks_per_10000)) +
geom_point(data = Starbucks_US,
aes( y = Latitude, x = Longitude),
size = 0.3,
alpha = 0.2,
color= "darkgreen") +
labs(title = "Number of Starbucks Locations per 10,000 People in the US",
x = "Longitude", y = "Latitude",
caption = "Map by Cat Terres: data from starbucks_us_by_state and census_pop_est_2018") +
expand_limits(x = states_map$long, y = states_map$lat) +
scale_fill_viridis_c(option = "A", direction = -1)
theme_map() +
theme(legend.background = element_blank(),
legend.title = "Per 1,000")
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A lot of the locations are concentrated on the west coast, with the highest ratio of Starbucks to people being in their home state of Washington.
leaflet)tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not. For an example of how to use tibble(), look at the favorite_stp_by_lisa I created in the data R code chunk at the beginning.favorite_places_cat <- tibble(
place = c("Home", "Macalester College", "Minneapolis Institute of Art",
"Cafe Latte", "Will and Corrie's House", "Half Price Books",
"Orchestra Hall", "Spyhouse Coffee", "Varsity Theater", "Spoon and Stable"),
long = c(-93.6492825600468, -93.1712321, -93.27321800238416,
-93.13631961957746, -93.31092454470381,-93.26624772751424,
-93.27450480662738, -93.16694786943424, -93.2368268023834, -93.26945531587614),
lat = c(45.20728090653907, 44.9378965, 44.95865191730926,
44.93990727249933, 45.212966410036486, 45.126287661067344,
44.97241014246441, 44.931189622822906, 44.980914347365605, 44.98569132243515),
top_three = c("Yes","No","Yes","Yes","No","No","No","No","No","No"))
leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.pal <- colorFactor("plasma",
domain = favorite_places_cat$top_three)
leaflet(data = favorite_places_cat) %>%
addTiles() %>%
addCircleMarkers(lng = ~long,
lat = ~lat,
color = ~pal(top_three),
label = ~place,
opacity = 10,
weight = 5) %>%
addLegend("topleft",
title = "In the top 3?",
pal = pal,
values = ~top_three)
pal <- colorFactor("plasma",
domain = favorite_places_cat$top_three)
leaflet(data = favorite_places_cat) %>%
addTiles() %>%
addCircleMarkers(lng = ~long,
lat = ~lat,
color = ~pal(top_three),
label = ~place,
opacity = 10,
weight = 5) %>%
addLegend("topleft",
title = "In the top 3?",
pal = pal,
values = ~top_three) %>%
addPolylines(lng = ~long,
lat = ~lat,
color = col2hex("darkblue"))
The data come from Washington, DC and cover the last quarter of 2014.
Two data tables are available:
Trips contains records of individual rentalsStations gives the locations of the bike rental stationsHere is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.
data_site <-
"https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds"
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")
Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.Stations %>%
summarize(min(lat),
max(lat),
min(long),
max(long))
bike_trips <- Trips %>%
group_by(sstation) %>%
summarise(count_station = n()) %>%
left_join(Stations,
by = c("sstation" = "name"))
world <- get_stamenmap(
bbox = c(left = -77.3, bottom = 38.78, right = -76.90, top = 39.15),
maptype = "terrain",
zoom = 11)
ggmap(world) +
geom_point(data = bike_trips,
aes(x = long, y = lat, color = count_station),
alpha = .7,
size = 1) +
labs(title = "Number of Bike Departures by Station",
col = "Number of Departures",
caption = "Map by Cat Terres") +
theme_map()
rental_users <- Trips %>%
group_by(sstation) %>%
mutate(binary = ifelse(client == "Casual", 1, 0)) %>%
summarize(count_station = n(),
prop = mean(binary)) %>%
left_join(Stations,
by = c("sstation" = "name"))
world <- get_stamenmap(
bbox = c(left = -77.3, bottom = 38.78, right = -76.90, top = 39.15),
maptype = "terrain",
zoom = 11)
ggmap(world) +
geom_point(data = rental_users,
aes(x = long,
y = lat,
color = prop),
alpha = .9,
size = 2) +
labs(title = "Proportion of Departures by Client Type at Each Station",
col = "Proportion",
caption = "Map by Cat Terres") +
theme_map()
Stations closest to the river have a higher proportion of casual users perhaps because people want to bike for leisure rather than transportation.
The following exercises will use the COVID-19 data from the NYT.
newest_covid <- covid19 %>%
arrange(desc(date)) %>%
group_by(state) %>%
mutate(numberrow = 1:n()) %>%
filter(numberrow == 1) %>%
mutate(state = str_to_lower(`state`))
states_map <-map_data("state")
newest_covid %>%
ggplot() +
geom_map(map = states_map,
aes(map_id = state,
fill = cases)) +
labs(title = "Most Recent Cumulative Count of COVID-19 Cases by State") +
expand_limits(x = states_map$long, y = states_map$lat) +
scale_fill_viridis_c(option = "rocket", direction = -1) +
theme_map() +
theme(legend.background = element_blank())
California, Texas, and Florida have the highest numbers of cumulative Covid deaths. I think this is a bit obvious and redundant and to improve the map, population density should be taken into account.
newest_covid <- covid19 %>%
arrange(desc(date)) %>%
group_by(state) %>%
mutate(numberrow = 1:n()) %>%
filter(numberrow == 1) %>%
mutate(state = str_to_lower(`state`))
covid_by_pop <- newest_covid %>%
left_join(census_pop_est_2018,
by = c("state" = "state")) %>%
mutate(per_10000 = (cases/est_pop_2018)*10000)
states_map <-map_data("state")
covid_by_pop %>%
ggplot() +
geom_map(map = states_map,
aes(map_id = state,
fill = per_10000)) +
labs(title = "Most Recent Cumulative Count of COVID-19 Cases by State Factoring in Population Density") +
expand_limits(x = states_map$long, y = states_map$lat) +
scale_fill_viridis_c(option = "rocket", direction = -1) +
theme_map() +
theme(legend.background = element_blank())
These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.
MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.mpls_suspicious <- MplsStops %>%
group_by(neighborhood) %>%
mutate(total_stops = sum(n()),
sus = ifelse(problem == "suspicious", 1, 0)) %>%
summarize(sus_proportion = sum(sus == 1)/n(),
total_stops=total_stops) %>%
filter(row_number(neighborhood) == 1) %>%
arrange(desc(total_stops))
mpls_suspicious
leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircleMarkers, set stroke = FAlSE, use colorFactor() to create a palette.pal2 <-colorFactor(palette = c("blue","orange"),
domain = MplsStops$problem)
leaflet(data = MplsStops) %>%
addProviderTiles(providers$Stamen.Toner) %>%
addCircleMarkers(lng = ~long,
lat = ~lat,
weight = 1,
radius = .5,
opacity = 1,
stroke = FALSE,
color = ~pal2(problem))
eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.mpls_nbhd <- st_read("Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp", quiet = TRUE)
mpls_joined <- mpls_nbhd %>%
left_join(mpls_suspicious,
by = c("BDNAME" = "neighborhood"))
mpls_all <- mpls_joined %>%
left_join(MplsDemo,
by = c("BDNAME" = "neighborhood"))
leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map.pal3<- colorNumeric(palette = "magma",
domain = mpls_all$sus_proportion)
leaflet(mpls_all) %>%
addTiles() %>%
addPolygons(
stroke = FALSE,
label = ~BDNAME,
fillColor = ~pal3(sus_proportion),
fillOpacity = .7) %>%
addLegend(pal = pal3,
values = ~sus_proportion,
opacity = NULL,
title = "Proportion",
position = "topleft")
South Minneapolis, as well as downtown, has a higher proportion of stops for suspicious activity.
leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows.I added a column to my favorite places table to display the frequency with which I’ve visited them in the last three months and colored my dots accordingly. So, where have I frequented the most in the past 3 months??
favorite_places_cat <- tibble(
place = c("Home", "Macalester College", "Minneapolis Institute of Art",
"Cafe Latte", "Will and Corrie's House", "Half Price Books",
"Orchestra Hall", "Spyhouse Coffee", "Varsity Theater", "Spoon and Stable"),
long = c(-93.6492825600468, -93.1712321, -93.27321800238416,
-93.13631961957746, -93.31092454470381,-93.26624772751424,
-93.27450480662738, -93.16694786943424, -93.2368268023834, -93.26945531587614),
lat = c(45.20728090653907, 44.9378965, 44.95865191730926,
44.93990727249933, 45.212966410036486, 45.126287661067344,
44.97241014246441, 44.931189622822906, 44.980914347365605, 44.98569132243515),
top_three = c("Yes","No","Yes","Yes","No","No","No","No","No","No"),
most_visited = c("Ten", "Forty Five", "Two", "Two", "One", "One", "Four", "One", "One", "Zero"))
pal5 <- colorFactor("viridis",
domain = favorite_places_cat$most_visited)
leaflet(data = favorite_places_cat) %>%
addTiles() %>%
addCircleMarkers(lng = ~long,
lat = ~lat,
color = ~pal5(most_visited),
label = ~place,
opacity = 10,
weight = 5) %>%
addLegend("topleft",
title = "Frequency",
pal = pal5,
values = ~most_visited)
https://github.com/cterres/Exercise4
DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?